Abstract
Technological advances in position aware devices increase the availability of tracking data of everyday objects such as animals, vehicles, people or football players. We propose a geographic data mining approach to detect generic aggregation patterns such as flocking behaviour and convergence in geospatial lifeline data. Our approach considers the object’s motion properties in an analytical space as well as spatial constraints of the object’s lifelines in geographic space. We discuss the geometric properties of the formalised patterns with respect to their efficient computation.
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References
Aurenhammer F (1991) Voronoi diagrams: A survey of a fundamental geometric data structure. ACM Comput. Surv. 23(3):345–405
Batty M, Desyllas J, Duxbury E (2003) The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades. Int. J. Geographical Information Systems 17(7):673–697
Bern M, Eppstein D (1997) Approximation algorithms for geometric problems. In Hochbaum DS (ed) Approximation Algorithms for NP-Hard Problems, PWS Publishing Company, Boston, MA, pp 296–345
Casaer J, Hermy M, Coppin P, Verhagen R (1999) Analysing space use patterns by Thiessen polygon and triangulated irregular network interpolation: a nonparametric method for processing telemetric animal fixes. Int. J. Geographical Information Systems 13(5):499–511
de Berg M, van Kreveld M, Overmars M, Schwarzkopf O (2000) Computational Geometry — Algorithms and Applications. Springer, Berlin, 2nd edition
Estivill-Castro V, Lee I (2002) Multi-level clustering and its visualization for exploratory data analysis. GeoInformatica 6(2):123–152
Ganskopp, D. (2001) Manipulating cattle distribution with salt and water in large arid-land pastures: a GPS/GIS assessment. Applied Animal Behaviour Science 73(4):251–262
Hornsby K, Egenhofer M (2002) Modeling moving objects over multiple granularities. Annals of Mathematics and Artificial Intelligence 36(1–2):177–194
Iwase S, Saito H (2002) Tracking soccer player using multiple views. In IAPR Workshop on Machine Vision Applications, MVA Proceedings, pp 102–105
Jain A, Duin R, Mao J (2000) Statistical pattern recognition: A review. IEEE Transactions on Pattern Recognition and Machine Intelligence 22(1):4–37
Laube P, Imfeld S (2002) Analyzing relative motion within groups of trackable moving point objects. In: Egenhofer M, Mark D (eds), Geographic Information Science, Second International Conference, GIScience 2002, Boulder, CO, USA, September 2002, LNCS 2478, Springer, Berlin, pp 132–144
Mark, D (2003) Geographic information science: Defining the field. In: Duckham M, Goodchild M, Worboys M (eds), Foundations of Geographic Information Science, chap. 1, Taylor and Francis, London New York, pp 3–18
Miller H (2003) What about people in geographic information science? Computers, Environment and Urban Systems 27(5):447–453
Miller H (2004) Tobler’s first law and spatial analysis. in preparation.
Miller H, Han J (2001) Geographic data mining and knowledge discovery: An overview. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery, Taylor and Francis, London New York, pp 3–32
Miller H, Wu Y (2000) GIS software for measuring space-time accessibility in transportation planning and analysis. GeoInformatica 4(2):141–159
Mountain D, Raper J (2001) Modelling human spatio-temporal behaviour: A challenge for location-based services. Proceedings of GeoComputation, Brisbane, 6
Openshaw S (1994) Two exploratory space-time-attribute pattern analysers relevant to GIS. In: Fotheringham S, Gogerson P (eds) GIS and Spatial Analysis, chap. 5, Taylor and Francis, London New York, pp 83–104
Openshaw S, Turton I, MacGill J (1999) Using geographic analysis machine to analyze limiting long-term illness census data. Geographical and Environmental Modelling 3(1):83–99
Pfoser D, Jensen C (1999) Capturing the uncertainty of moving-object representations. In: Gueting R, Papadias D, Lochowsky, F (eds) Advances in Spatial Databases, 6th International Symposium, SSD’99, Hong Kong, China, July 1999. LNCS 1651, Springer, Berlin Heidelberg, pp 111–131
Ramos E (1999) On range reporting, ray shooting and k-level construction. In: Proc. 15th Annu. ACM Symp. on Computational Geometry, pp 390–399
Roddick J, Hornsby K, Spiliopoulou M (2001) An updated bibliography of temporal, spatial, and spatio-temporal data mining research. In: Roddick J, Hornsby K (eds), Temporal, spatial and spatio-temporal data mining, TSDM 2000, LNAI 2007, Springer, Berlin Heidelberg, pp 147–163
Sibbald AM, Hooper R, Gordon IJ, Cumming S (2001) Using GPS to study the effect of human disturbance on the behaviour of the red deer stags on a highland estate in Scotland. In: Sibbald A, and Gordon IJ (eds) Tracking Animals with GPS, Macaulay Institute, pp 39–43
Smyth C (2001) Mining mobile trajectories. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery, Taylor and Francis, London New York, pp 337–361
Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Economic Geography 46(2):234–240
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Laube, P., van Kreveld, M., Imfeld, S. (2005). Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines. In: Developments in Spatial Data Handling. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26772-7_16
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DOI: https://doi.org/10.1007/3-540-26772-7_16
Publisher Name: Springer, Berlin, Heidelberg
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